SOTAVerified

Prompt Engineering

Prompt engineering is the process of designing and refining the prompts used to generate text from language models, such as GPT-3 or similar models. The goal of prompt engineering is to improve the quality and relevance of the generated text by carefully crafting the prompts to elicit the desired responses from the model.

Prompt engineering involves several steps, including selecting the appropriate model architecture and parameters, designing the prompt format and structure, selecting the appropriate task and training data, and fine-tuning the model using the selected prompt and data.

Prompt engineering is a crucial step in the development of language models, as it can greatly influence the quality and effectiveness of the model's responses. By carefully designing and refining the prompts used to generate text, researchers and developers can improve the accuracy and relevance of the model's output, making it more useful for a wide range of applications, including chatbots, language translation, content creation, and more.

Papers

Showing 501–550 of 1236 papers

TitleStatusHype
AI-Driven Virtual Teacher for Enhanced Educational Efficiency: Leveraging Large Pretrain Models for Autonomous Error Analysis and Correction—0
Propaganda is all you needCode0
LLM-based Weak Supervision Framework for Query Intent Classification in Video Search—0
Contri(e)ve: Context + Retrieve for Scholarly Question Answering—0
What Should We Engineer in Prompts? Training Humans in Requirement-Driven LLM UseCode1
Fine-tuning Large Language Models for Entity MatchingCode1
LLM Honeypot: Leveraging Large Language Models as Advanced Interactive Honeypot SystemsCode0
DrLLM: Prompt-Enhanced Distributed Denial-of-Service Resistance Method with Large Language ModelsCode0
Fine-tuning and Prompt Engineering with Cognitive Knowledge Graphs for Scholarly Knowledge Organization—0
Beyond designer's knowledge: Generating materials design hypotheses via large language models—0
Multimodal Large Language Model Driven Scenario Testing for Autonomous Vehicles—0
ChatGPT's Potential in Cryptography Misuse Detection: A Comparative Analysis with Static Analysis Tools—0
Insights from Benchmarking Frontier Language Models on Web App Code GenerationCode1
Combining LLMs and Knowledge Graphs to Reduce Hallucinations in Question Answering—0
Entity Extraction from High-Level Corruption Schemes via Large Language ModelsCode0
RETAIN: Interactive Tool for Regression Testing Guided LLM Migration—0
Leveraging Large Language Models through Natural Language Processing to provide interpretable Machine Learning predictions of mental deterioration in real time—0
BEAVER: An Enterprise Benchmark for Text-to-SQL—0
Language Models Benefit from Preparation with Elicited Knowledge—0
Enhancing AI-Driven Psychological Consultation: Layered Prompts with Large Language Models—0
Bootstrap Segmentation Foundation Model under Distribution Shift via Object-Centric LearningCode0
LLM-assisted Labeling Function Generation for Semantic Type Detection—0
Evaluating Named Entity Recognition Using Few-Shot Prompting with Large Language ModelsCode1
Toward Automated Simulation Research Workflow through LLM Prompt Engineering DesignCode1
Visual Prompt Engineering for Medical Vision Language Models in Radiology—0
Toward Large Language Models as a Therapeutic Tool: Comparing Prompting Techniques to Improve GPT-Delivered Problem-Solving Therapy—0
HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure ModelingCode2
Atoxia: Red-teaming Large Language Models with Target Toxic Answers—0
Vision-Language and Large Language Model Performance in Gastroenterology: GPT, Claude, Llama, Phi, Mistral, Gemma, and Quantized ModelsCode0
Towards Reliable Medical Question Answering: Techniques and Challenges in Mitigating Hallucinations in Language Models—0
Can LLM be a Good Path Planner based on Prompt Engineering? Mitigating the Hallucination for Path Planning—0
Understanding Defects in Generated Codes by Language Models—0
What Do You Want? User-centric Prompt Generation for Text-to-image Synthesis via Multi-turn GuidanceCode0
Controllable Text Generation for Large Language Models: A SurveyCode3
Towards Analyzing and Mitigating Sycophancy in Large Vision-Language Models—0
Probabilistic Medical Predictions of Large Language Models—0
EEG-Defender: Defending against Jailbreak through Early Exit Generation of Large Language Models—0
EPiC: Cost-effective Search-based Prompt Engineering of LLMs for Code GenerationCode1
GS-KGC: A Generative Subgraph-based Framework for Knowledge Graph Completion with Large Language Models—0
V-RoAst: Visual Road Assessment. Can VLM be a Road Safety Assessor Using the iRAP Standard?Code1
Revisiting VerilogEval: A Year of Improvements in Large-Language Models for Hardware Code GenerationCode3
QPO: Query-dependent Prompt Optimization via Multi-Loop Offline Reinforcement Learning—0
GoNoGo: An Efficient LLM-based Multi-Agent System for Streamlining Automotive Software Release Decision-Making—0
When Raw Data Prevails: Are Large Language Model Embeddings Effective in Numerical Data Representation for Medical Machine Learning Applications?—0
Dual-Domain CLIP-Assisted Residual Optimization Perception Model for Metal Artifact Reduction—0
Fast Training Dataset Attribution via In-Context Learning—0
Leveraging Language Models for Emotion and Behavior Analysis in Education—0
Document-Level Event Extraction with Definition-Driven ICL—0
FiSTECH: Financial Style Transfer to Enhance Creativity without Hallucinations in LLMs—0
Can GPT-4 Models Detect Misleading Visualizations?—0
Show:102550
← PrevPage 11 of 25Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PromptKDHarmonic mean77.62—Unverified
2Customized EnsembleHarmonic mean75.49—Unverified
3MMRLHarmonic mean74.45—Unverified
4MMRL++Harmonic mean74.44—Unverified
5CoPromptHarmonic mean74.33—Unverified
6HPT++Harmonic mean74.24—Unverified
7HPTHarmonic mean74.17—Unverified
8ProMetaRHarmonic mean74.09—Unverified
9MetaPromptHarmonic mean74.02—Unverified
10DePTHarmonic mean74.02—Unverified
#ModelMetricClaimedVerifiedStatus
1PromptKDHarmonic mean97.77—Unverified
2HPT++Harmonic mean96.96—Unverified
3MMRL++Harmonic mean96.75—Unverified
4MMRLHarmonic mean96.68—Unverified
5HPTHarmonic mean96.65—Unverified
6CoPromptHarmonic mean96.55—Unverified
7MetaPromptHarmonic mean96.32—Unverified
8DePTHarmonic mean96.28—Unverified
9ProMetaRHarmonic mean96.16—Unverified
10RPOHarmonic mean96.03—Unverified
#ModelMetricClaimedVerifiedStatus
1PromptKDHarmonic mean77.94—Unverified
2MMRL++Harmonic mean74.46—Unverified
3HPT++Harmonic mean74.23—Unverified
4MMRLHarmonic mean73.82—Unverified
5CoPromptHarmonic mean72.79—Unverified
6ProMetaRHarmonic mean72.31—Unverified
7HPTHarmonic mean72.16—Unverified
8PromptSRCHarmonic mean71.75—Unverified
9DePTHarmonic mean71.09—Unverified
10RPOHarmonic mean68.61—Unverified
#ModelMetricClaimedVerifiedStatus
1MMRL++Harmonic mean91.94—Unverified
2PromptKDHarmonic mean89.14—Unverified
3HPT++Harmonic mean87.36—Unverified
4MMRLHarmonic mean87.21—Unverified
5CoPromptHarmonic mean85.84—Unverified
6ProMetaRHarmonic mean85.3—Unverified
7DePTHarmonic mean84.88—Unverified
8HPTHarmonic mean84.82—Unverified
9MetaPromptHarmonic mean83.38—Unverified
10MaPLeHarmonic mean82.35—Unverified
#ModelMetricClaimedVerifiedStatus
1PromptKDHarmonic mean45.17—Unverified
2MMRL++Harmonic mean42.24—Unverified
3HPT++Harmonic mean41.33—Unverified
4MMRLHarmonic mean41.15—Unverified
5DePTHarmonic mean40.73—Unverified
6HPTHarmonic mean40.28—Unverified
7ProMetaRHarmonic mean40.25—Unverified
8PromptSRCHarmonic mean40.15—Unverified
9CoPromptHarmonic mean39.76—Unverified
10MetaPromptHarmonic mean38.24—Unverified
#ModelMetricClaimedVerifiedStatus
1PromptKDHarmonic mean90.24—Unverified
2HPTHarmonic mean87.16—Unverified
3MMRL++Harmonic mean87.01—Unverified
4MMRLHarmonic mean86.78—Unverified
5ProMetaRHarmonic mean86.7—Unverified
6DePTHarmonic mean86.46—Unverified
7PromptSRCHarmonic mean85.95—Unverified
8HPT++Harmonic mean85.85—Unverified
9CoPromptHarmonic mean85.71—Unverified
10MetaPromptHarmonic mean84.52—Unverified
#ModelMetricClaimedVerifiedStatus
1PromptKDHarmonic mean97.15—Unverified
2HPT++Harmonic mean96.91—Unverified
3CoPromptHarmonic mean96.87—Unverified
4MMRLHarmonic mean96.74—Unverified
5HPTHarmonic mean96.71—Unverified
6MaPLeHarmonic mean96.58—Unverified
7MMRL++Harmonic mean96.51—Unverified
8ProMetaRHarmonic mean96.49—Unverified
9CoCoOpHarmonic mean96.43—Unverified
10DePTHarmonic mean96.37—Unverified
#ModelMetricClaimedVerifiedStatus
1PromptKDHarmonic mean83.13—Unverified
2MMRL++Harmonic mean78.18—Unverified
3MMRLHarmonic mean78.06—Unverified
4DePTHarmonic mean77.79—Unverified
5ProMetaRHarmonic mean76.72—Unverified
6PromptSRCHarmonic mean76.58—Unverified
7CoPromptHarmonic mean75.66—Unverified
8HPT++Harmonic mean75.59—Unverified
9HPTHarmonic mean75.57—Unverified
10MetaPromptHarmonic mean75.48—Unverified
#ModelMetricClaimedVerifiedStatus
1PromptKDHarmonic mean82.6—Unverified
2CoPromptHarmonic mean81.31—Unverified
3MMRL++Harmonic mean81.28—Unverified
4MMRLHarmonic mean81.2—Unverified
5HPT++Harmonic mean81.11—Unverified
6DePTHarmonic mean81.06—Unverified
7HPTHarmonic mean80.88—Unverified
8ProMetaRHarmonic mean80.82—Unverified
9MetaPromptHarmonic mean80.62—Unverified
10PromptSRCHarmonic mean80.52—Unverified
#ModelMetricClaimedVerifiedStatus
1PromptKDHarmonic mean86.1—Unverified
2MMRLHarmonic mean83.89—Unverified
3HPT++Harmonic mean83.81—Unverified
4MMRL++Harmonic mean83.81—Unverified
5ProMetaRHarmonic mean83.25—Unverified
6HPTHarmonic mean83.16—Unverified
7CoPromptHarmonic mean83.07—Unverified
8PromptSRCHarmonic mean82.74—Unverified
9DePTHarmonic mean82.46—Unverified
10MetaPromptHarmonic mean81.35—Unverified
#ModelMetricClaimedVerifiedStatus
1PromptKDHarmonic mean93.05—Unverified
2CoPromptHarmonic mean91.4—Unverified
3MaPLeHarmonic mean91.38—Unverified
4ProMetaRHarmonic mean91.34—Unverified
5MetaPromptHarmonic mean91.29—Unverified
6DePTHarmonic mean91.22—Unverified
7MMRL++Harmonic mean91.1—Unverified
8PromptSRCHarmonic mean91.1—Unverified
9HPT++Harmonic mean91.09—Unverified
10MMRLHarmonic mean91.03—Unverified
#ModelMetricClaimedVerifiedStatus
1POMPTop-1 accuracy %51.6—Unverified
2MMRLTop-1 accuracy %51.2—Unverified
3HPT++Top-1 accuracy %51.18—Unverified
4MaPLeTop-1 accuracy %50.9—Unverified
5PromptSRCTop-1 accuracy %50.9—Unverified
6HPTTop-1 accuracy %50.85—Unverified
7CoCoOpTop-1 accuracy %50.63—Unverified
8CoPromptTop-1 accuracy %50.5—Unverified
9CLIPTop-1 accuracy %47.77—Unverified
#ModelMetricClaimedVerifiedStatus
1POMPTop-1 accuracy %77.9—Unverified
2PromptSRCTop-1 accuracy %77.8—Unverified
3MMRLTop-1 accuracy %77.53—Unverified
4HPT++Top-1 accuracy %77.52—Unverified
5CoPromptTop-1 accuracy %77.51—Unverified
6HPTTop-1 accuracy %77.38—Unverified
7MaPLeTop-1 accuracy %76.98—Unverified
8CoCoOPTop-1 accuracy %76.18—Unverified
9CLIPTop-1 accuracy %73.96—Unverified
#ModelMetricClaimedVerifiedStatus
1POMPTop-1 accuracy %49.8—Unverified
2PromptSRCTop-1 accuracy %49.55—Unverified
3CoPromptTop-1 accuracy %49.43—Unverified
4HPTTop-1 accuracy %49.36—Unverified
5HPT++Top-1 accuracy %49.28—Unverified
6MMRLTop-1 accuracy %49.17—Unverified
7MaPLeTop-1 accuracy %49.15—Unverified
8CoCoOpTop-1 accuracy %48.75—Unverified
9CLIPTop-1 accuracy %46.15—Unverified
#ModelMetricClaimedVerifiedStatus
1HPT++Top-1 accuracy %65.31—Unverified
2HPTTop-1 accuracy %65.25—Unverified
3MMRLTop-1 accuracy %64.47—Unverified
4PromptSRCTop-1 accuracy %64.35—Unverified
5CoCoOpTop-1 accuracy %64.07—Unverified
6MaPLeTop-1 accuracy %64.07—Unverified
7POMPTop-1 accuracy %63.8—Unverified
8CLIPTop-1 accuracy %60.83—Unverified
#ModelMetricClaimedVerifiedStatus
1POMPAccuracy25.3—Unverified
2VPTAccuracy24.8—Unverified